// Copyright (c) 2023 Huawei Technologies Co., Ltd
// All rights reserved.
//
// Licensed under the BSD 3-Clause License  (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include "op_plugin/AclOpsInterface.h"
#include "op_plugin/OpApiInterface.h"
#include "op_plugin/utils/op_api_common.h"

namespace op_api {
using npu_preparation = at_npu::native::OpPreparation;

at::Tensor _ctc_loss_backward(
    const at::Tensor& grad_out,
    const at::Tensor& log_probs,
    const at::Tensor& targets,
    at::IntArrayRef input_lengths,
    at::IntArrayRef target_lengths,
    const at::Tensor& neg_log_likelihood,
    const at::Tensor& log_alpha,
    int64_t blank,
    bool zeroInfinity) {
  DO_COMPATIBILITY(aclnnCtcLossBackward, acl_op::_ctc_loss_backward(grad_out, log_probs, targets, input_lengths,
                                                                    target_lengths, neg_log_likelihood, log_alpha,
                                                                    blank, zeroInfinity));

  auto output_size = op_infer::input_same_output_size(log_probs);

  // construct the output tensor of the NPU
  at::Tensor grad = npu_preparation::apply_tensor_without_format(grad_out, output_size);

  // calculate the output result of the NPU
  EXEC_NPU_CMD(aclnnCtcLossBackward, grad_out, log_probs, targets, input_lengths, target_lengths,
      neg_log_likelihood, log_alpha, blank, zeroInfinity, grad);

  return grad;
}
} // namespace op_api
